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computed in WordNet. The final semantic relatedness score l of two words that combines WordNet and word
embedding is defined as in Eq.1:

1
rel(w , w ) = max cos (v(w ), v(w )) + (1 − λ) (1)
i
j
j
i
m,n λ dist(Sw , Sw )
j
i
Where dist (Si, Sj) is the distance between two senses Si, m, and Sj, n. v(w ), v(w ) are the vector
j
i
representations of word wi and wj in the word embedding. λ is a weighting factor. The final combined word to
combined sense representation model is defined as in Eq. 2:

W2CS(w) = {w |w ∶ rel(w , w ) > t } (2)
j
j
i
j
1

3.2. Step 2: SRT enrichment to build Social Requirement Terms Vocabulary SRTV

In this work, SynSet WordNet is used to enrich SRT by adding the synonymous words onto the existing
keywords to achieve the Social Requirement Terms Vocabulary (SRTV). Eventually, it will increase the
distinct gap between related reviews and non-related reviews in classifying the reviews of the applications.
SRTV contains a group of words labeled to each term of SRT, as shown in Figure 2.
SRT SRTV label
Privacy Word 1
Embedding Privacy, confidentiality, ········
&
Freedom SynSet Freedom, independence, ······· 2
WordNet

·
Fig. 2. Enriching SRT to build SRTV.

3.3. Step 3: Build BOR by relating SRTV label with SMA review
The Bag-of-Requirements or BOR (as shown in Figure 3) is a SMA review that has been vectorized into
BOW and n-gram and labeled with SRTV. The semi-supervised KNN approach applied to the vectorized SRTV
and reviews to achieve multi BOR labeling for each review.

Apps label Reviews SRTV label

Facebook It forces me to put all my personal and private information 1,2,6
including my phone number.

WhatsApp It used to be a place to go to connect with friends and 2,4,9
colleagues and there were freedoms used every day. Now it is
not a place that you can speak freely.

Fig. 3. Bag-of-Requirement data organization.

E- Proceedings of The 5th International Multi-Conference on Artificial Intelligence Technology (MCAIT 2021) [190]
Artificial Intelligence in the 4th Industrial Revolution